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Sound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models. Sci Rep 2021; 11:11155. [PMID: 34045622 PMCID: PMC8160169 DOI: 10.1038/s41598-021-90734-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 05/17/2021] [Indexed: 11/16/2022] Open
Abstract
This paper proposes a data augmentation method based on artificial intelligence (AI) to obtain sound level spectrum as predicting the spatial and temporal data of time-resolved three-dimensional Particle Tracking Velocimetry (4D PTV) data. A 4D PTV has used to measure flow characteristics of three side mirror models adopting the Shake-The-Box (STB) algorithm with four high-speed cameras on a robotic arm for measuring industrial scale. Helium filled soap bubbles are used as tracers in the wind tunnel experiment to characterize flow structures around automobile side mirror models. Full volumetric velocity fields and evolution of vortex structures are obtained and analyzed. Instantaneous pressure fields are deduced by solving a Poisson equation based on the 4D PTV data. To predict spatial and temporal data of velocity field, artificial intelligence (AI)-based data prediction method has applied. Adaptive Neural Fuzzy Inference System (ANFIS) based machine learning algorithm works well to find 4D missing data behind the automobile side mirror model. Using the ANFIS model, power spectrum of velocity fluctuations and sound level spectrum of pressure fluctuations are successfully obtained to assess flow and noise characteristics of three different side mirror models.
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Babanezhad M, Taghvaie Nakhjiri A, Rezakazemi M, Marjani A, Shirazian S. Functional input and membership characteristics in the accuracy of machine learning approach for estimation of multiphase flow. Sci Rep 2020; 10:17793. [PMID: 33082441 PMCID: PMC7575550 DOI: 10.1038/s41598-020-74858-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 10/08/2020] [Indexed: 11/19/2022] Open
Abstract
In the current study, Artificial Intelligence (AI) approach was used for the learning of a physical system. We applied four inputs and one output in the learning process of AI. In the learning process, the inputs are space locations of a BCR (bubble column reactor), which are x, y, and z coordinate as well as the amount of gas fraction in BCR. The liquid velocity is also considered as output. A variety of functions were used in learning, such as gbellmf and gaussmf functions, to examine which functions can give the best learning. At the end of the study, all of the results were compared to CFD (computational fluid dynamics). A three-dimensional (3D) BCR was used in this research, and we studied simulation by CFD as well as AI. The data from CFD in a 3D BCR was studied in the AI domain. In AI, we tuned for various parameters to achieve the best intelligence in the system. For instance, different inputs, different membership functions, different numbers of membership functions were used in the learning process. Moreover, the meshless prediction was used, meaning that some data in the BCR have not participated in the learning, and they were predicted in the prediction process, which gives us a special capability to compare the results with the CFD outcomes. The findings showed us that AI can predict the CFD results, and a great agreement was achieved between CFD computing nodes and AI elements. This novel methodology can suggest a meshless and multifunctional AI model to simulate the turbulence flow in the BCR. For further evaluation, the ANFIS method is compared with ACOFIS and PSOFIS methods with regards to model’s accuracy. The results show that ANFIS method contains higher accuracy and prediction capability compared with ACOFIS and PSOFIS methods.
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Affiliation(s)
- Meisam Babanezhad
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.,Faculty of Electrical - Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam
| | - Ali Taghvaie Nakhjiri
- Department of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mashallah Rezakazemi
- Faculty of Chemical and Materials Engineering, Shahrood University of Technology, Shahrood, Iran
| | - Azam Marjani
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam. .,Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Saeed Shirazian
- Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland.,Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin Prospekt, Chelyabinsk, Russia, 454080
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ANFIS grid partition framework with difference between two sigmoidal membership functions structure for validation of nanofluid flow. Sci Rep 2020; 10:15395. [PMID: 32958774 PMCID: PMC7505986 DOI: 10.1038/s41598-020-72182-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 08/27/2020] [Indexed: 11/09/2022] Open
Abstract
In this study, a square cavity is modeled using Computational Fluid Dynamics (CFD) as well as artificial intelligence (AI) approach. In the square cavity, copper (Cu) nanoparticle is the nanofluid and the flow velocity characteristics in the x-direction and y-direction, and the fluid temperature inside the cavity at different times are considered as CFD outputs. CFD outputs have been assessed using one of the artificial intelligence algorithms, such as a combination of neural network and fuzzy logic (ANFIS). As in the ANFIS method, we have a non-dimension procedure in the learning step, and there is no issue in combining other characteristics of the flow and thermal distribution beside the x and y coordinates, we combine two coordinate parameters and one flow parameter. This ability of method can be considered as a meshless learning step that there is no instability of the numerical method or limitation of boundary conditions. The data were classified using the grid partition method and the MF (membership function) type was dsigmf (difference between two sigmoidal membership functions). By achieving the appropriate intelligence in the ANFIS method, output prediction was performed at the points of cavity which were not included in the learning process and were compared to the existing data (the results of the CFD method) and were validated by them. This new combination of CFD and the ANFIS method enables us to learn flow and temperature distribution throughout the domain thoroughly, and eventually predict the flow characteristics in short computational time. The results from AI in the ANFIS method were compared to the ant colony and fuzzy logic methods. The data from CFD results were inserted into the ant colony system for the training process, and we predicted the data in the fuzzy logic system. Then, we compare the data with the ANFIS method. The results indicate that the ANFIS method has a high potentiality compared to the ant colony method because the amount of R in the ANIFS system is higher than R in the ant colony method. In the ANFIS method, R is equal to 0.99, and in the ant colony method, R is equal to 0.91. This shows that the ant colony needs more time for both the prediction and training of the system. Also, comparing the pattern recognition in the two systems, we can obviously see that by using the ANFIS method, the predictions completely match the target points. But the other method cannot match the flow pattern and velocity distribution with the CFD method.
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Babanezhad M, Zabihi S, Taghvaie Nakhjiri A, Marjani A, Behroyan I, Shirazian S. Prediction of Nanofluid Characteristics and Flow Pattern on Artificial Differential Evolution Learning Nodes and Fuzzy Framework. ACS OMEGA 2020; 5:22091-22098. [PMID: 32923767 PMCID: PMC7482090 DOI: 10.1021/acsomega.0c02121] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 08/13/2020] [Indexed: 06/11/2023]
Abstract
A combination of a fuzzy inference system (FIS) and a differential evolution (DE) algorithm, known as the differential evolution-based fuzzy inference system (DEFIS), is developed for the prediction of natural heat transfer in Cu-water nanofluid within a cavity. In the development of the hybrid model, the DE algorithm is used for the training process of FIS. For this purpose, first, the case study is simulated using the computational fluid dynamic (CFD) method. The CFD outputs, including velocity in the y-direction, the temperature of the nanofluid, and the nanoparticle content (Ø), are employed for the learning process of the DEFIS model. By choosing the optimum number of inputs and the number of population, the underlying DEFIS variable parameters are studied. After reaching the high value of DEFIS intelligence, in the learning step, a variety of Ø values (e.g., 0.5, 1, and 2) are reviewed. For the full intelligence of DEFIS, the velocity of the nanofluid is predicted in further nodes of the cavity domain. Finally, the velocity of the nanofluid is predicted by using the data at Ø = 0.15, which are absent in the DEFIS process.
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Affiliation(s)
- Meisam Babanezhad
- Institute
of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
- Faculty
of Electrical−Electronic Engineering, Duy Tan University, Da Nang 550000, Vietnam
| | - Samyar Zabihi
- Department
of Process Engineering, Research and Development Department, Shazand-Arak Oil Refinery Company, Arak 38671-41111, Iran
| | - Ali Taghvaie Nakhjiri
- Department
of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
| | - Azam Marjani
- Department
of Chemistry, Arak Branch, Islamic Azad
University, Arak 31136-98562, Iran
| | - Iman Behroyan
- Mechanical
and Energy Engineering Department, Shahid
Beheshti University, Tehran 1983969411, Iran
| | - Saeed Shirazian
- Department
for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty
of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
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Nabipour N, Babanezhad M, Taghvaie Nakhjiri A, Shirazian S. Prediction of Nanofluid Temperature Inside the Cavity by Integration of Grid Partition Clustering Categorization of a Learning Structure with the Fuzzy System. ACS OMEGA 2020; 5:3571-3578. [PMID: 32118172 PMCID: PMC7045517 DOI: 10.1021/acsomega.9b03911] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 02/03/2020] [Indexed: 05/31/2023]
Abstract
In this study, a quadratic cavity is simulated using computational fluid dynamics (CFD). The simulated cavity includes nanofluids containing copper (Cu) nanoparticles. The L-shaped thermal element exists in this cavity to produce heat distribution along with the domain. Results such as fluid velocity distribution in two dimensions and the fluid temperature field were generated as CFD simulation results. These outputs were evaluated using an adaptive neuro-fuzzy inference system (ANFIS) for learning and then the prediction process. In the training process related to the ANFIS method, x coordinates, y coordinates, and fluid temperature are three inputs, and the fluid velocity in line with Y is the output. During the learning process, the data have been classified using a clustering method called grid clustering. In line with the attempt to rise ANFIS intelligence, the alterations in the number of input parameters and of membership structure have been analyzed. After reaching the highest level of intelligence, the fluid velocity nodes were predicted to be in line with y, especially cavity nodes, which were absent in CFD simulations. The simulation findings indicated that there is a good agreement between CFD and clustering approach, while the total simulation time for learning and prediction is shorter than the time needed for calculation using the CFD method.
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Affiliation(s)
- Narjes Nabipour
- Institute
of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
| | - Meisam Babanezhad
- Department
of Energy, Faculty of Mechanical Engineering, South Tehran Branch, Islamic Azad University, 1584743311 Tehran, Iran
| | - Ali Taghvaie Nakhjiri
- Department
of Chemical Engineering, Science and Research Branch, Islamic Azad University, 1477893855 Tehran, Iran
| | - Saeed Shirazian
- Department
for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty
of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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